A quick guide to understanding web testing

If you've ever wondered why your digital marketing efforts haven't panned out, the fault may be in your lack of testing. Successful Internet entrepreneurs live and die by meticulous testing and tweaking every aspect of their digital campaigns. Until they find the combination of words, backlinks, media, and tone that draws the biggest returns. If you treat every marketing campaign as an experiment, you may find the success you've been missing. There are two basic forms of testing used in digital marketing analysis. Both have something to offer any individual or company looking for the best way to sell their products or services.

A/B Testing

A/B Testing, which is also called split testing, is a method of website optimization that compares the conversion rates of two versions of a page using live traffic. Site visitors are distributed across the two different page versions. By tracking the way a visitor interacts with the page you can determine which version of the page is the most effective. A/B testing helps you measure the impact of a single change in relation to your page goals. Though called A/B testing, the marketer can widen the scope of the experiment by adding a third page version, a fourth, and so on.

The following is an example of A/B testing at work:Walt has decided to jumpstart his baseball website's success by creating a Google Adwords account. He chooses his keywords through careful research and bids enough so his ads will get some front page exposure. But Walt realizes that no matter how much time he spends perfecting his ad, he will never know how effective the ad is without an A/B test. To that end, he creates a second ad, changing only a single word. Once he's measured the impact of the two ads, he creates a third one, again changing only that single word. Everything else – the keywords, the bid price, the rest of the phrasing – remains the same.

A/B Testing is the least complex method of evaluating a page design, and is useful in a variety of situations. One of the most common ways A/B testing is utilized is to test two very different design directions against one another. For example, the current version of a company's home page might have in-text calls to action, while the new version might eliminate most text, but include a new top bar advertising the latest product. After enough visitors have been funneled to both pages, the number of clicks on each page's version of the call to action can be compared. It's important to note that even though many design elements are changed in this kind of A/B test, only the impact of the design as a whole on each page's business goal is tracked, not individual elements.

A/B testing is also useful as an optimization option for pages where only one element is up for debate. For example, a pet store running an A/B test on their site might find that 85% more users are willing to sign up for a newsletter held up by a cartoon mouse than they are for one emerging from the coils of a boa constrictor. When A/B testing is used in this way, a third or even fourth version of the page is often included in the test, which is sometimes called an A/B/C/D test. This, of course, means that traffic to the site must be split into thirds or fourths, with a lesser percentage of visitors visiting each site.

Simple in concept and design, A/B testing is a powerful and widely used testing method. Keeping the number of tracked variables small means these tests can deliver reliable data very quickly, as they do not require a large amount of traffic to run. This is especially helpful if your site has a small number of daily visitors. Splitting traffic into more than three or four segments would make it hard to finish a test. In fact, A/B testing is so speedy and easy to interpret that some large sites use it as their primary testing method, running cycles of tests one after another rather than more complex multivariate tests.

A/B testing is also a good way to introduce the concept of optimization through testing to a skeptical marketing team, as it can quickly demonstrate the quantifiable impact of a simple design change.
A/B testing is a versatile tool, and when paired with smart experiment design and a commitment to iterative cycles of testing and redesign, it can help you make huge improvements to your site. However, it is important to remember that the limitations of this kind of test are summed up in the name. A/B testing is best used to measure the impact of a two to four variables on interactions with the page. Tests with more variables take longer to run, and in and of itself, A/B testing will not reveal any information about interaction between variables on a single page.If you need to test the interactions of multiple elements within a page then multivariate testing may be the best approach.

Multivariate Testing (MVT)

Multivariate testing uses the same core mechanism as A/B testing, but looks at variables within a single page. Live traffic sent to a single page and is split between the different versions of the design. The purpose of a multivariate test, then, is to measure the effectiveness each design combination has on a marketing goal. Once a site has received enough traffic to run the test, the data from each variation is compared to find not only the most successful design, but also to determine which page elements have the greatest positive or negative impact on a visitor's interaction.

Multivariate testing allows a greater range of freedom in your marketing hypotheses. For marketers who like to optimize their campaigns in as little time as possible, MVT can be among the best ways to do so. Instead of changing a single variable, multivariate testing allows you to change as many as you want.

The following is an example of multivariate testing at work:Jesse is gearing up to roll out a new campaign page. There has been a lot of discussion in the marketing department about the campaign and the landing page. The final page contains a sign up form, a campaign header, and footer. Unfortunately, many people are concerned that the campaign header won’t resonate with the target audience and the sign up form is way too long. Jess realizes that no matter how much time she spends on perfecting the page she will never know how effective the page is without an MVT test. To that end she simply modifies the page design to create variants that include two different lengths of a sign up form, three different headers and two footers. During the test all visitors are funneled to all the possible combinations of these elements with the same page. Jesse is also running a full factorial test and is one of the reasons why multivariate testing is often recommended only for sites that have a substantial amount of daily traffic — the more variations that need to be tested, the longer it takes to obtain meaningful data from the test.

After the test has been run, the variables on each page variation are compared to each other, and to their performance in the context of other versions of the test. What emerges is a clear picture of which variations are best performing, and which elements are most responsible for this performance. For example, varying page footer may be shown to have very little effect on the performance of the page, while varying the length of the sign-up form has a huge impact. Multivariate testing is a great way to help you target redesign efforts to the elements of your page where they will have the most impact. This is especially useful when designing landing page campaigns, for example, as the data about the impact of a certain element's design can be applied to future campaigns, even if the context of the element has changed.

The single biggest limitation of multivariate testing is the amount of traffic needed to complete the test. Since all experiments are fully factorial, too many changing elements at once can quickly add up to a very large number of possible combinations that must be tested. Even a site with fairly high traffic might have trouble completing a test with more than 25 combinations in a feasible amount of time.

When using multivariate tests, it's also important to consider how they will fit into your cycle of testing and redesign as a whole. Even when you are armed with information about the impact of a particular element, you may want to do additional A/B testing cycles to explore other radically different ideas. Also, sometimes it may not be worth the extra time necessary to run a full multivariate test when several well-designed A/B tests will do the job well.